Programing with Deep Neural Networks dneuraln

Worker #1

dneuraln / bayes_by_backprop.py

Created December 2, 2016 02:23 — forked from rocknrollnerd/bayes_by_backprop.py

Theano implementation of Bayes-by-Backprop algorithm from "Weight uncertainty in neural networks" paper

	import theano
	import theano.tensor as T
	from theano.tensor.shared_randomstreams import RandomStreams
	from theano.sandbox.rng_mrg import MRG_RandomStreams
	from lasagne.updates import adam
	from lasagne.utils import collect_shared_vars

	from sklearn.datasets import fetch_mldata
	from sklearn.cross_validation import train_test_split
	from sklearn import preprocessing

dneuraln / Amortized_Explaining_Away_Demo.ipynb

Created February 3, 2017 08:49 — forked from fhuszar/Amortized_Explaining_Away_Demo.ipynb

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

dneuraln / tf_lstm.py

Created May 13, 2017 10:34 — forked from siemanko/tf_lstm.py

Simple implementation of LSTM in Tensorflow in 50 lines (+ 130 lines of data generation and comments)

	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation:

dneuraln / TheNumericsOfGANs.ipynb

Created October 8, 2017 16:41 — forked from LMescheder/TheNumericsOfGANs.ipynb

This notebook contains the code for the toy experiment in the paper The Numerics of GANs.

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.